1. Field of the Invention
The present invention relates to an optical neural network apparatus using a primary processing, and more specifically to an optical neural network apparatus capable of expanding a limited input range by extracting the characteristic feature of input information using the primary processing for an input of the optical neural network apparatus and of easily dealing with also a mutual recall type associative memory system using a different input/output relation.
2. Description of the Prior Art
There have recently been accumulated earnest studies to realize a new processing architecture on the basis upon a neural network model that is a model of an information processing system in a human brain, the new processing architecture being different from a conventional Von Neumann type computer architecture. The neural network model is a large-scale parallel computing one assuming a neuron model as a basic unit, and exhibits, as a characteristic feature, an information processing function that is self-organizable autonomously.
In order to essentially realize the architecture of the neural network, it is therefore necessary to achieve complete parallel processing with respect to all processing mechanisms such as a computing mechanism, networks, and input and output units.
To realize such complete parallel processing, optical computing and optical connection have attracted attention which utilize as an information medium light demonstrating higher spatial parallel properties in principle. According to the technique which utilizes light, large-scale parallel computing and high density parallel connection are achievable. Much interest is therefore put on this method as being an effective hardware to realize the large-scale parallel computing capability of the neural network model.
As associative memory model, in particular, in the neural network model exhibits a uniformly parallel computing architecture so that there is a possibility of realizing a high density computing and interconnecting capability utilizing the advantages of the optical computing.
The inventors have proposed an optical associative memory system incorporating a learning function called an optical associatron, in the Japanese Society of the Electronic Information Communication held 1988, July 7 and in Japanese Patent Laid-Open Publications No. 64-78491, and No. 63-307437, etc. The optical associatron has achieved highly adaptive associative memory through optical computing by utilizing the analog parallel computing and memory function of a microchannel spatial light modulator tube and introducing an orthogonal learning method. Additionally, there have been reported, instead of such an optical associatron, many trials to realize the associative memory system using the large-scale parallel properties of light (for example as disclosed in Japanese Patent Laid-Open Publication No. 1-112225).
Herein, the associative memory system means a kind of content addressable memory system in which many patterns are stored in a memory device in overlapping and only part of a necessary pattern is fed to a computing device, so that only the necessary pattern is separated and fetched from the memory device. Use of such an associative memory system enables associative processing through a vague input, that is inadvantageous for prior computers, to be realized and computing time to be sharply reduced.
The prior optical associative memory system, however, suffers from a difficulty that input cells are limited to a small number, say, 4.times.4=16 in the foregoing optical associatron without permitting a two-dimensional (2-D) image to be inputted intactly, and hence an allowable input range is limited. Additionally, the prior optical associatron incorporates an auto associative memory system using the same member for an input/output relationship and hence fails in learning and association with a wide input/output relationship.
In contrast, there is widely known a technique to subject input information to the primary processing. For example, with image data taken as the input information, edge extraction, thinning, magnification, compression, rotation, and the like are performed as the primary processing. Additionally, primary processing with use of an optical system is also performed in which a two-dimensional coherent image is transmitted by a lens, whereby an optical Fourier transformation image of an original image can be formed on a focal surface in real time. Accordingly, application of the primary processing to optical computing was intended up to now. However, the optical computing (Fourier transformation, for example), although it enjoys a high speed owing to its high parallel property, suffers from a difficulty in matching with a next stage computer, that has only a logical processing capability as the feature, provided an optical system exhibits low accuracy or an image subjected to the primary processing includes any vague property, with a result that it is difficult in handling and hence it presents little availability.